The DOI matches its Crossref record; 157 of the 157 references it cites were found in a registry or an index.
Understanding brain networks and brain organization
Open accessAbstract
What is the relationship between brain and behavior? The answer to this question necessitates characterizing the mapping between structure and function. The aim of this paper is to discuss broad issues surrounding the link between structure and function in the brain that will motivate a network perspective to understanding this question. However, as others in the past, I argue that a network perspective should supplant the common strategy of understanding the brain in terms of individual regions. Whereas this perspective is needed for a fuller characterization of the mind-brain, it should not be viewed as panacea. For one, the challenges posed by the many-to-many mapping between regions and functions is not dissolved by the network perspective. Although the problem is ameliorated, one should not anticipate a one-to-one mapping when the network approach is adopted. Furthermore, decomposition of the brain network in terms of meaningful clusters of regions, such as the ones generated by community-finding algorithms, does not by itself reveal "true" subnetworks. Given the hierarchical and multi-relational relationship between regions, multiple decompositions will offer different "slices" of a broader landscape of networks within the brain. Finally, I described how the function of brain regions can be characterized in a multidimensional manner via the idea of diversity profiles. The concept can also be used to describe the way different brain regions participate in networks.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“multiple structural network configurations can theoretically give rise to the same apparent aberrations in function – this is essentially the ‘many-to-many mappings’ problem very nicely described in (Pessoa, 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Pessoa, 2014
In the citing paper
“There are numerous lines of evidence to suggest that brain-behavior relationships are not simply one-to-one (Pessoa, 2014).”
From Investigating functional brain network integrity using a traditional and novel categorical scheme for neurodevelopmental disorders (2019) · cites this paper as Pessoa, 2014
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A Mathematical Theory of Communication
Shannon, C. E. (1948) · Bell System Technical Journal · doi:10.1002/j.1538-7305.1948.tb01338.x · Cited by 88,486
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060
Community structure in social and biological networks
Girvan, M., & Newman, M. E. J. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.122653799 · Cited by 15,502 · cited 1×
“Communities are determined by subdividing a network into groups of nodes, often by maximizing the number of within-group links, and minimizing the number of between-group links [142].”
BackgroundComplex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820 · cited 1×
“In the past decade, graph-theoretical analysis of functional neuroimaging data has focused almost exclusively on characterizing the large-scale properties of resting-state data [122,123].”
BackgroundComplex network measures of brain connectivity: Uses and interpretations
Rubinov, M., & Sporns, O. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2009.10.003 · Cited by 11,522 · cited 2×
“For detailed treatments, please see [62]; for applications to brain data, please see [66].”
BackgroundResultFunctional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553 · cited 1×
“It is noteworthy, for instance, that the task-positive network and the task-negative network display fairly complementary profiles, matching the intuition that these networks are at times “anti-correlated”[158, 159].”
Parallel organization of functionally segregated circuits linking basal ganglia and cortex
Alexander, G. E., DeLong, M. R., & Strick, P. L. (1986) · Annual Review of Neuroscience · doi:10.1146/annurev.ne.09.030186.002041 · Cited by 8,527 · cited 2×
“Using a similar approach, Postuma and Dagher [120] identified patterns of co-activation between the cortex and striatum that appear to reflect the cortico-striatal loops proposed by Alexander and colleagues [67].”
BackgroundMethodDistributed hierarchical processing in the primate cerebral cortex
Felleman, D. J., & Van Essen, D. C. (1991) · Cerebral Cortex · doi:10.1093/cercor/1.1.1 · Cited by 7,783
Flagged first, then by citations
Research works printed but not linked: 1
- Fox, 2005, The human brain is intrinsically organized into dynamic, anticorrelated functional networks, Proc Natl Acad Sci USA, 102, 9673, 10.1073/pnas.0504136102 · DOI · Paper page
Cited by
Sorted by citations
Demystifying cognitive flexibility: Implications for clinical and developmental neuroscience
Dajani, D. R., & Uddin, L. Q. (2015) · Trends in Neurosciences · doi:10.1016/j.tins.2015.07.003 · Cited by 1,046
Small-World Brain Networks Revisited
Bassett, D. S., & Bullmore, E. T. (2016) · The Neuroscientist · doi:10.1177/1073858416667720 · Cited by 887
“…of nervous systems since 2006, as evidenced by rapid growth in research articles, reviews and citations related to“brain graphs” and “connectomes” [20–22]; by the publication of several textbooks [23, 24]; and by the recent launch of new specialist journals for network neuroscience [25].”
BackgroundDynamic reconfiguration of frontal brain networks during executive cognition in humans
Braun, U., Schäfer, A., Walter, H., Erk, S., Romanczuk-Seiferth, N., Haddad, L., Schweiger, J. I., Grimm, O., Heinz, A., Tost, H., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1422487112 · Cited by 791
Farahani, F. V., Karwowski, W., & Lighthall, N. R. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00585 · Cited by 765
“…approaches have opened new avenues toward studying the neural mechanisms underlying human cognition and behavior from the view of interregional brain interactions (Park and Friston, 2013; Pessoa, 2014; Sporns, 2014; Medaglia et al., 2015; Petersen and Sporns, 2015; Kriegeskorte and Douglas, 2018).”
BackgroundLutz, A., Jha, A. P., Dunne, J. D., & Saron, C. D. (2015) · American Psychologist · doi:10.1037/a0039585 · Cited by 697
“…these phenomenological dimensions and activity in specific brain regions is plausible because studies concerning emotion and cognition have shown that brain regions’ functions are inherently multidimensional, and that structurally different regions can perform the same function (Pessoa, 2014).”
BackgroundTowards a universal taxonomy of macro-scale functional human brain networks
Uddin, L. Q., Yeo, B. T. T., & Spreng, R. N. (2019) · Brain Topography · doi:10.1007/s10548-019-00744-6 · Cited by 683
“One subsystem has been neuro-anatomically referred to as the medial temporal lobe subsystem (Andrews-Hanna et al. 2010, 2014).”
InfluentialDixon, M. L., De La Vega, A., Mills, C., Andrews-Hanna, J., Spreng, R. N., Cole, M. W., & Christoff, K. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1715766115 · Cited by 595
Mocanu, D. C., Mocanu, E., Stone, P., Nguyen, P. H., Gibescu, M., & Liotta, A. (2018) · Nature Communications · doi:10.1038/s41467-018-04316-3 · Cited by 524
“Bycontrast to ANNs, biological neural networks have been demonstrated to have a sparse (rather than dense) topology 4,5 , and also hold other important properties that are instrumental to learning ef fi ciency.”
A Network Model of the Emotional Brain
Pessoa, L. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.03.002 · Cited by 562
Self-citationGrayson, D. S., & Fair, D. A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.01.079 · Cited by 411
“The network's behavior can thus be modeled as a set of properties that emerge from the network's unique global structure and local features (Sporns, 2014).”
BackgroundSmall-world human brain networks: Perspectives and challenges
Liao, X., Vasilakos, A. V., & Yong He, A. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.03.018 · Cited by 443
Cliques and cavities in the human connectome
Sizemore, A. E., Giusti, C., Kahn, A. E., Vettel, J. M., Betzel, R. F., & Bassett, D. S. (2017) · Journal of Computational Neuroscience · doi:10.1007/s10827-017-0672-6 · Cited by 384
“2014), though there are exceptions that give the brain its topological properties such as its small-world architecture (Bassett and Bullmore 2006; Pessoa 2014; Hilgetag and Goulas 2016; Muldoon et al. 2016a; Bassett and Bullmore 2016).”
BackgroundJiang, L., & Zuo, X. (2015) · The Neuroscientist · doi:10.1177/1073858415595004 · Cited by 338
“This implicit graphical methodology offers several advantages in preserving weak connectivity in functional connectomics (Jiang and others 2015b; Pessoa 2014).”
Faskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00719-y · Cited by 341
Beyond the Tripartite Cognition–Emotion–Interoception Model of the Human Insular Cortex
Uddin, L. Q., Kinnison, J., Pessoa, L., & Anderson, M. L. (2014) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00462 · Cited by 275
Self-citationUnderstanding the behavioural consequences of noninvasive brain stimulation
Bestmann, S., de Berker, A. O., & Bonaiuto, J. J. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.10.003 · Cited by 255
Masouleh, S. K., Eickhoff, S. B., Hoffstaedter, F., Genon, S., & Initiative, A. D. N. (2019) · eLife · doi:10.7554/elife.43464 · Cited by 229
“Following this philosophical segregation between psychological sciences and neurosciences, it is now widely acknowledged that there is no one-to-one mapping between behavioral functions and brain regions (Anderson, 2016; Genon et al., 2018; Pessoa, 2014).”
Linking interindividual variability in brain structure to behaviour
Genon, S., Eickhoff, S. B., & Masouleh, S. K. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00584-7 · Cited by 193
Basic Emotions in Human Neuroscience: Neuroimaging and Beyond
Celeghin, A., Diano, M., Bagnis, A., Viola, M., & Tamietto, M. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.01432 · Cited by 202
“However, it is becoming increasingly clear that the correspondence between brain structure and function is better represented by a oneto-many relationship, or pluripotentiality (Gallese and Lakoff, 2005; Dehaene and Cohen, 2007; Gallese, 2008; Hurley, 2008; Pessoa, 2008, 2014; Anderson, 2016).”
BackgroundSprooten, E., Rasgon, A., Goodman, M. C., Carlin, A., Leibu, E., Lee, W. H., & Frangou, S. (2017) · Human Brain Mapping · doi:10.1002/hbm.23486 · Cited by 189
“However, the degree to which mental processes engage specific, common or partially overlapping regions remains a topic of debate [Pessoa, 2014; Price and Friston, 2005].”
BackgroundJaniri, D., Moser, D. A., Doucet, G. E., Luber, M. J., Rasgon, A., Lee, W. H., Murrough, J. W., Sani, G., Eickhoff, S. B., & Frangou, S. (2020) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2019.3351 · Cited by 173
Nomi, J. S., Farrant, K., Damaraju, E., Rachakonda, S., Calhoun, V. D., & Uddin, L. Q. (2016) · Human Brain Mapping · doi:10.1002/hbm.23135 · Cited by 180
“These network approaches to brain function have recently dominated the cognitive neuroscience landscape, and are continuing to provide more nuanced views of structure-function relationships in the human brain [Pessoa, 2014].”
BackgroundHasson, U., Egidi, G., Marelli, M., & Willems, R. M. (2018) · Cognition · doi:10.1016/j.cognition.2018.06.018 · Cited by 157
“In a conceptual review of functional networks, Pessoa (2014) outlined the foundations for such a vocabulary.”
ALE meta‐analysis reveals dissociable networks for affective and discriminative aspects of touch
Morrison, I. (2016) · Human Brain Mapping · doi:10.1002/hbm.23103 · Cited by 164
“In particular, the posterior-anterior insula axis may contribute to affective evaluation in terms of salience [Menon and Uddin, 2010; Pessoa, 2014], certainty, and/or risk, as has been postulated for the case of pain [Mouraux et al., 2011; Morrison et al., 2013; Perini et al., 2013].”
BackgroundThe embodied brain: towards a radical embodied cognitive neuroscience
Kiverstein, J. D., & Miller, M. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00237 · Cited by 181
“Pessoa has suggested that ‘‘In the extreme, two networks may involve the exact same regions interacting with each other in distinct ways across time’’ (Pessoa, 2014, p. 408).”
BackgroundInfluentialSubcortical contributions to large-scale network communication
Bell, P. T., & Shine, J. M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.036 · Cited by 168
A nice surprise? Predictive processing and the active pursuit of novelty
Clark, A. (2018) · Phenomenology and the Cognitive Sciences · doi:10.1007/s11097-017-9525-z · Cited by 156
“Understanding that role requires us to move beyond what ((Pessoa 2014) p.11) describes as the ‘corticocentric’ image in which evolutionary older sub-cortical structures are dominated and controlled by the more recent cortical overlay.”
BackgroundFace processing in autism spectrum disorders: From brain regions to brain networks
Nomi, J. S., & Uddin, L. Q. (2015) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2015.03.029 · Cited by 168
“…perspective to understanding brain function can provide more parsimonious explanations of the complex relationship between brain and behavior (Pessoa, 2014; Linquist and Barrett, 2012) and in particular can elucidate key principals of functional brain organization underlying typical and…”
BackgroundRichardson, M., McEwan, K., Maratos, F., & Sheffield, D. (2016) · Evolutionary Psychological Science · doi:10.1007/s40806-016-0065-5 · Cited by 155
“However, this approach is not without criticism, and Pessoa (2014) suggests that given its rich connectivity, the amygdala is arguably a processing hub belonging to a minimum of at least three networks pertaining to visual processing, autonomic awareness and the generation of bodily states, and a…”
BackgroundDiano, M., Tamietto, M., Celeghin, A., Weiskrantz, L., Tatu, K., Bagnis, A., Duca, S., Geminiani, G. C., Cauda, F., & Costa, T. (2017) · Scientific Reports · doi:10.1038/srep45260 · Cited by 148
Chronnectomic patterns and neural flexibility underlie executive function
Nomi, J. S., Vij, S. G., Dajani, D. R., Steimke, R., Damaraju, E., Rachakonda, S., Calhoun, V. D., & Uddin, L. Q. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.026 · Cited by 131
Understanding emotion with brain networks
Pessoa, L. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.09.005 · Cited by 155
Self-citationZhang, H., Chen, X., Shi, F., Li, G., Kim, M., Giannakopoulos, P., Haller, S., & Shen, D. G. (2016) · Journal of Alzheimer s Disease · doi:10.3233/jad-160092 · Cited by 142
“However, after regressing out the task evoked BOLD responses, the data was suitable for FC analysis, where the patterns of FC were proven to be comparable to those in conventional rs-fMRI studies [52] (see Supplementary Material 2 for details).”
InfluentialHappily entangled: prediction, emotion, and the embodied mind
Miller, M., & Clark, A. (2018) · Synthese · doi:10.1007/s11229-017-1399-7 · Cited by 114
“…that target cortical regions either directly or via the thalamus, including areas such as the basal forebrain (Dunnett et al., 1991), hypothalamus (Pessoa, 2014), basal ganglia (Clarke et al., 2008), amygdala (Pessoa, 2013), cerebellum (Leiner et al., 1986), and brainstem via dopaminergic and…”
BackgroundInfluentialIndividual differences and time-varying features of modular brain architecture
Liao, X., Cao, M., Xia, M., & Yong He, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.066 · Cited by 113
Najafi, M., McMenamin, B. W., Simon, J. Z., & Pessoa, L. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.054 · Cited by 115
Self-citationControversies and progress on standardization of large-scale brain network nomenclature
Uddin, L. Q., Betzel, R. F., Cohen, J. R., Damoiseaux, J. S., Brigard, F. D., Eickhoff, S. B., Fornito, A., Gratton, C., Gordon, E. M., Laird, A. R., et al. (2023) · Network Neuroscience · doi:10.1162/netn_a_00323 · Cited by 125
“Likewise, hub regions in the brain are thought to dynamically affiliate with disparate clusters of brain regions in a contextdependent fashion (Cole et al., 2013; Pessoa, 2014).”
BackgroundSelf-citationRDoC-based categorization of amygdala functions and its implications in autism
Hennessey, T., Andari, E., & Rainnie, D. G. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.04.007 · Cited by 107
“…is a prime example of how neuroscience has shifted from a phrenological perspective mapping one function—fear—to one brain region and towards a network paradigm where actions arise from a dynamic affiliation of neural assemblages communicating with one another (Pessoa, 2014; Weiskrantz, 1956).”
Pessoa, L., Medina, L., Hof, P. R., & Desfilis, E. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.09.021 · Cited by 105
“Specific regions do not work in isolation but are part of large-scale connectional systems that support a rich repertoire of functions (Pessoa, 2014).”
BackgroundSelf-citationThe functional overlap of executive control and language processing in bilinguals
Coderre, E. L., Smith, J. F., van Heuven, W. J. B., & Horwitz, B. (2015) · Bilingualism Language and Cognition · doi:10.1017/s1366728915000188 · Cited by 114
“See Pessoa, 2014 and Horwitz, 2014 for further discussion of brain networks.”
BackgroundRefocusing neuroscience: moving away from mental categories and towards complex behaviours
Pessoa, L., Medina, L. M., & Desfilis, E. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0534 · Cited by 106
Self-citationEarly human brain development: Starring the subplate
Hadders-Algra, M. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2018.06.017 · Cited by 100
“2; Modha and Singh, 2010; Pessoa, 2014).”
InfluentialVenkatesh, M., JáJá, J. F., & Pessoa, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116398 · Cited by 98
Self-citationLi, H., Satterthwaite, T. D., & Yong Fan, A. (2018) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi.2018.8363532 · Cited by 95
“On the other hand, datadriven brain decomposition methods have provided alternative tools for charactering intrinsic connectivity networks (ICNs) and estimate functional network connectivity and are favored for modeling many-to-many mapping between brain regions and functions [11].”
MethodA contextualist approach to functional localization in the brain
Burnston, D. C. (2016) · Biology & Philosophy · doi:10.1007/s10539-016-9526-2 · Cited by 98
“MT, for instance, can be said to represent at least motion, coarse depth, and fine depth depending on the stimulus context, and to respond to color when that information helps segment a moving stimulus. Read in this way, there is ample evidence that decomposition can be met. I will here briefly compare current physiol…”
BackgroundResultInfluentialJiang, Y., Li, S., Li, X., Zhang, Y., Chen, Y., Jiang, S., Hou, C., Yao, D., Wang, X., & Luo, C. (2019) · Human Brain Mapping · doi:10.1002/hbm.24584 · Cited by 85
“, 2013), and many-to-many (Pessoa, 2014) have not been fully explored yet.”
BackgroundMicrofluidics for Neuronal Cell and Circuit Engineering
Habibey, R., Arias, J. E. R., Striebel, J., & Busskamp, V. (2022) · Chemical Reviews · doi:10.1021/acs.chemrev.2c00212 · Cited by 88
Précis on The Cognitive-Emotional Brain
Pessoa, L. (2015) · Behavioral and Brain Sciences · doi:10.1017/s0140525x14000120 · Cited by 84
Self-citationA topological insight into restricted Boltzmann machines
Mocanu, D. C., Mocanu, E., Nguyen, P. H., Gibescu, M., & Liotta, A. (2016) · Machine Learning · doi:10.1007/s10994-016-5570-z · Cited by 79
“Lately, the neural networks of the human brain have started to be studied using tools from network science (Pessoa, 2014).”
BackgroundLarge-scale sparse functional networks from resting state fMRI
Li, H., Satterthwaite, T. D., & Yong Fan, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.05.004 · Cited by 77
“overlap between FNs obtained by the proposed method, indicating that certain brain regions might play multiple roles in the brain networks, especially higher-order association cortex within the frontal and temporal lobes (Pessoa, 2014).”
Cellular and Molecular Mechanisms Linking Human Cortical Development and Evolution
Libé-Philippot, B., & Vanderhaeghen, P. (2021) · Annual Review of Genetics · doi:10.1146/annurev-genet-071719-020705 · Cited by 78
Individual‐level brain morphological similarity networks: Current methodologies and applications
Cai, M., Ma, J., Wang, Z., Zhao, Y., Zhang, Y., Wang, H., Xue, H., Chen, Y., Zhang, Y., Wang, C., et al. (2023) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.14384 · Cited by 78
Subcortical-cortical dynamical states of the human brain and their breakdown in stroke
Favaretto, C., Allegra, M., Deco, G., Metcalf, N. V., Griffis, J. C., Shulman, G. L., Brovelli, A., & Corbetta, M. (2022) · Nature Communications · doi:10.1038/s41467-022-32304-1 · Cited by 77
Xu, J., Potenza, M. N., Calhoun, V. D., Zhang, R., Yip, S. W., Wall, J. T., Pearlson, G. D., Worhunsky, P. D., Garrison, K. A., & Moran, J. M. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.08.035 · Cited by 68
“This GLM approach and its findings are in general consistent with a modular view of brain functional organization that different mental processes operate in different brain regions (Donoso et al., 2014; Kanwisher, 2010; Pessoa, 2014; Rowland and Moser, 2014; Szentagothai, 1983).”
ResultActivation network mapping for integration of heterogeneous fMRI findings
Peng, S., Xu, P., Jiang, Y., & Gong, G. (2022) · Nature Human Behaviour · doi:10.1038/s41562-022-01371-1 · Cited by 70
Effects of virtual reality-based motor rehabilitation: a systematic review of fMRI studies
Feitosa, J. A., Fernandes, C. A., Casseb, R. F., & Castellano, G. (2021) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac456e · Cited by 69
Hidalgo‐Lopez, E., Mueller, K., Harris, T., Aichhorn, M., Sacher, J., & Pletzer, B. A. (2020) · Brain Structure and Function · doi:10.1007/s00429-019-02019-z · Cited by 69
“Despite the complex relation between structural and functional connectivity (Pessoa 2014), given the acute trophic effects on dendritic spine density and consequent synapse formation, these effects would also be expected to reflect on brain function and connectivity (Kitamura et al.”
Bioinspired nanofluidic iontronics for brain-like computing
Yu, L., Li, X., Luo, C., Lei, Z., Wang, Y., Hou, Y., Wang, M., & Hou, X. (2023) · Nano Research · doi:10.1007/s12274-023-5900-y · Cited by 67
Brain network characterization of high-risk preterm-born school-age children
Fischi-Gómez, E., Muñoz‐Moreno, E., Vasung, L., Griffa, A., Borradori-Tolsa, C., Monnier, M., Lazeyras, F., Thiran, J., & Hüppi, P. S. (2016) · NeuroImage Clinical · doi:10.1016/j.nicl.2016.02.001 · Cited by 64
The Kantian brain: brain dynamics from a neurophenomenological perspective
Fazelpour, S., & Thompson, E. T. (2014) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2014.12.006 · Cited by 61
Individual differences in valence bias: fMRI evidence of the initial negativity hypothesis
Petro, N. M., Tong, T. T., Henley, D. J., & Neta, M. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy049 · Cited by 67
“Indeed, it has been demonstrated that one single area may not be explicative of the cognitive functions underlying the processing of a stimulus, instead the strength of the connections may vary and be more informative (Pessoa, 2014; Diano et al., 2017).”
BackgroundCortical thickness patterns as state biomarker of anorexia nervosa
Lavagnino, L., Mwangi, B., Cao, B., Shott, M. E., Soares, J. C., & Frank, G. (2018) · International Journal of Eating Disorders · doi:10.1002/eat.22828 · Cited by 60
“Brain organization refers to how brain regions form networks, and larger or smaller brain regions therefore contribute differently to larger networks that are thought to drive behavior (Pessoa, 2014).”
BackgroundBrucar, L. R., Feczko, E., Fair, D. A., & Zilverstand, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.12.020 · Cited by 60
Psychiatric sequelae of traumatic brain injury — future directions in research
Li, L. M., Carson, A. J., & Dams-O’Connor, K. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00853-8 · Cited by 62
The Diversity and Multiplexity of Edge Communities within and between Brain Systems
Jo, Y., Esfahlani, F. Z., Faskowitz, J., Chumin, E. J., Sporns, O., & Betzel, R. F. (2020) · Cell Reports · doi:10.1016/j.celrep.2021.110032 · Cited by 59
Di, X., Huang, J., & Biswal, B. B. (2016) · Brain Structure and Function · doi:10.1007/s00429-016-1239-4 · Cited by 61
“Therefore, it is not a one-to-one mapping between function and connectivity (Pessoa 2014).”
BackgroundA functorial Dowker theorem and persistent homology of asymmetric networks
Chowdhury, S., & Mémoli, F. (2018) · Journal of Applied and Computational Topology · doi:10.1007/s41468-018-0020-6 · Cited by 75
“…2010), commerce and economy (Elliott et al. 2014; Easley and Kleinberg 2010; Acemoglu et al. 2015), neuroscience (Sporns 2011, 2012; Sporns and Kötter 2004; Rubinov and Sporns 2010; Pessoa 2014), biology (Barabási and Oltvai 2004; Huson et al. 2010), and defence (Masys 2014), to name a few sources.”
BackgroundConnectivity‐based parcellation of normal and anatomically distorted human cerebral cortex
Doyen, S., Nicholas, P. J., Poologaindran, A., Crawford, L. S., Young, I. M., Romero-García, R., & Sughrue, M. E. (2021) · Human Brain Mapping · doi:10.1002/hbm.25728 · Cited by 56
“A unifying principle of brain network organization is that the functional relevance of a brain area is dependent on its connectivity with surrounding brain areas (Fischl, 2012; McIntosh, 2004; Pessoa, 2014).”
BackgroundSurlari, Z., Budală, D. G., Lupu, I. C., Stelea, C. G., Butnaru, O., & Luchian, I. A. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12237378 · Cited by 55
“The human brain can be conceptualized as a complex network of interconnected neurons that communicate with one another, transmitting electrical impulses throughout the entire body [2].”
BackgroundClosing the mechanistic gap: the value of microarchitecture in understanding cognitive networks
Paquola, C., Amunts, K., Evans, A. C., Smallwood, J., & Bernhardt, B. C. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2022.07.001 · Cited by 52
Commitment System Theory: The Evolving Structure of Commitments to Multiple Targets
Klein, H. J., Solinger, O. N., & Duflot, V. (2020) · Academy of Management Review · doi:10.5465/amr.2018.0031 · Cited by 66
Holczer, A., Németh, V. L., Vékony, T., Vécsei, L., Klivènyi, P., & Must, A. (2020) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2020.00179 · Cited by 56
“Brain regions organize into brain networks to implement various cognitive and other operations (Pessoa, 2014).”
BackgroundSparse evolutionary deep learning with over one million artificial neurons on commodity hardware
Liu, S., Mocanu, D. C., Matavalam, A. R. R., Pei, Y., & Pechenizkiy, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05136-7 · Cited by 67
“Sparse evolutionary training Inspired by the fact that biological neural networks are prone to be sparse, rather than dense (Strogatz, 2001; Pessoa, 2014), there is an increasing interest in conceiving neural networks with a sparse topology (Mocanu et al.”
BackgroundCongenital blindness is associated with large-scale reorganization of anatomical networks
Hasson, U., Andric, M., Atılgan, H., & Collignon, O. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.12.048 · Cited by 51
Brain flexibility associated with need for cognition contributes to creative achievement
He, L., Zhuang, K., Li, Y., Sun, J., Meng, J., Zhu, W., Mao, Y., Chen, Q., Chen, X., & Qiu, J. (2019) · Psychophysiology · doi:10.1111/psyp.13464 · Cited by 52
On the interplay between mathematics and biology
Bellomo, N., Elaiw, A. M., Althiabi, A. M., & Alghamdi, M. (2014) · Physics of Life Reviews · doi:10.1016/j.plrev.2014.12.002 · Cited by 50
Tasks activating the default mode network map multiple functional systems
Mancuso, L., Cavuoti-Cabanillas, S., Liloia, D., Manuello, J., Buzi, G., Duca, S., Cauda, F., & Costa, T. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02467-0 · Cited by 53
“There are several obvious reasons explaining why canonical RSNs are a simplistic representation of brain complexity (Pessoa 2014).”
Building an EEG-fMRI Multi-Modal Brain Graph: A Concurrent EEG-fMRI Study
Yu, Q., Wu, L., Bridwell, D. A., Erhardt, E. B., Du, Y., He, H., Chen, J., Liu, P., Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T., Pearlson, G. D., et al. (2016) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2016.00476 · Cited by 49
Affect-biased attention and predictive processing
Ransom, M., Fazelpour, S., Markovic, J., Kryklywy, J. H., Thompson, E. T., & Todd, R. M. (2020) · Cognition · doi:10.1016/j.cognition.2020.104370 · Cited by 49
“For example, they follow (Pessoa, 2014) in viewing the medial pulvinar as amplifying weak but biologically valuable signals and so influencing behavior.”
BackgroundHossein, S., Cooper, J. A., DeVries, B. A. M., Nuutinen, M. R., Hahn, E., Kragel, P. A., & Treadway, M. T. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02056-5 · Cited by 53
“Within this system, the amygdala has been indicated as a network “ hub ” , given its high connectedness with multiple cortical areas in primates [12, 13].”
BackgroundA connectome-based mechanistic model of focal cortical dysplasia
Hong, S., Lee, H.-M., Gill, R. S., Crane, J., Sziklas, V., Bernhardt, B. C., Bernasconi, N., & Bernasconi, A. (2019) · Brain · doi:10.1093/brain/awz009 · Cited by 48
Reis, J. C., Antoni, M. H., & Travado, L. (2020) · CNS Spectrums · doi:10.1017/s1092852918001621 · Cited by 44
Burrows, C. A., Timpano, K. R., & Uddin, L. Q. (2017) · Clinical Psychological Science · doi:10.1177/2167702616683506 · Cited by 43
“Current conceptualizations of brain function contend that local and distal brain regions are functionally coupled, and operate within networks to achieve specific outcomes (Biswal, 526 Burrows et al. Zerrin Yetkin, Haughton, & Hyde, 1995; Damoiseaux et al., 2006; Pessoa, 2014; Smith et al., 2009).”
BackgroundReliability of graph metrics derived from resting‐state human EEG
Kuntzelman, K. M., & Miskovic, V. (2016) · Psychophysiology · doi:10.1111/psyp.12600 · Cited by 43
Detection of Pilot’s Mental Workload Using a Wireless EEG Headset in Airfield Traffic Pattern Tasks
Liu, C., Zhang, C., Sun, L., Liu, K., Liu, H., Zhu, W., & Jiang, C. (2023) · Entropy · doi:10.3390/e25071035 · Cited by 43
Zhang, J., Lin, X., Fu, G., Sai, L., Chen, H., Wang, M., Wang, M., Liu, Q., Yang, G., Zhang, J., et al. (2016) · Scientific Reports · doi:10.1038/srep25297 · Cited by 43
Emotion and the Interactive Brain: Insights From Comparative Neuroanatomy and Complex Systems
Pessoa, L. (2018) · Emotion Review · doi:10.1177/1754073918765675 · Cited by 58
“From that viewpoint, what is important is not simply the anatomical location of a region but its position in a space of functional relationships to other regions (Aertsen & Preissl, 1991; Friston et al., 1997; Pessoa, 2014).”
BackgroundInfluentialSelf-citationvan Schie, C. C., van Harmelen, A., Hauber, K., Boon, A. E., Crone, E. A., & Elzinga, B. M. (2017) · European Journal of Psychotraumatology · doi:10.1080/20008198.2016.1272788 · Cited by 38
“Finally, as the mirror and mentalization system involve a network of brain regions, we should understand neural findings in the context of a larger network (Bressler & Menon, 2010; Pessoa, 2014).”
Deep Variational Autoencoder for Mapping Functional Brain Networks
Qiang, N., Dong, Q., Ge, F., Liang, H., Ge, B., Zhang, S., Sun, Y., Gao, J., & Liu, T. (2020) · IEEE Transactions on Cognitive and Developmental Systems · doi:10.1109/tcds.2020.3025137 · Cited by 42
Altered brain networks in psychogenic erectile dysfunction: a resting‐state fMRI study
Chen, J., Chen, Y., Chen, G., Dai, Y., Yao, Z., & Lü, Q. (2017) · Andrology · doi:10.1111/andr.12411 · Cited by 38
Amend, M., Ionescu, T. M., Di, X., Pichler, B. J., Biswal, B. B., & Wehrl, H. F. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.034 · Cited by 36
Toward an integrative science of social vision in intergroup bias
Bagnis, A., Celeghin, A., Mosso, C. O., & Tamietto, M. (2019) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2019.04.020 · Cited by 38
Mills, B. D., Grayson, D. S., Shunmugavel, A., Miranda-Domínguez, Ó., Feczko, E., Earl, E., Neve, K. A., & Fair, D. A. (2018) · Journal of Neuroscience · doi:10.1523/jneurosci.2910-17.2018 · Cited by 33
“As others have noted, identifying causal links between structural and functional network disturbance is fundamentally difficult (43) and depends crucially on the validity of the measurements and the proposed model (5, 25).”
BackgroundGeier, C., Lehnertz, K., & Bialonski, S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00462 · Cited by 36
“ORIGINAL RESEARCH published: 20 August 2015 doi: 10.3389/fnhum.2015.00462 Frontiers in Human Neuroscience | www.frontiersin.org 1 August 2015 | Volume 9 | Article 462 Edited by: Joydeep Bhattacharya, Goldsmiths, University of London, UK Reviewed by: Sabato Santaniello, University of Connecticut, USA Huafu Chen, Univer…”
InfluentialDajani, D. R., Burrows, C. A., Odriozola, P., Baez, A., Nebel, M. B., Mostofsky, S. H., & Uddin, L. Q. (2019) · NeuroImage: Clinical · doi:10.1016/j.nicl.2019.101678 · Cited by 33
“There are numerous lines of evidence to suggest that brain-behavior relationships are not simply one-to-one (Pessoa, 2014).”
Qiang, N., Dong, Q., Liang, H., Ge, B., Zhang, S., Zhang, C., Gao, J., & Sun, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-021-06868-w · Cited by 35
Brooks, S. J., Parks, S. M., & Stamoulis, C. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab126 · Cited by 40
An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network
Fayaz, M., Torokeldiev, N., Turdumamatov, S., Qureshi, M. S., Qureshi, M. B., & Gwak, J. (2021) · Sensors · doi:10.3390/s21227480 · Cited by 37
“The brain connectivity characterizes networks of brain regions connected by anatomical traits [4,5].”
BackgroundBishop, R. C., Silberstein, M., & Pexton, M. (2022) · doi:10.1093/oso/9780192849786.001.0001 · Cited by 36
Gao, C., Weber, C. E., & Shinkareva, S. V. (2019) · Cortex · doi:10.1016/j.cortex.2019.05.016 · Cited by 35
Metadata
- Authors
- Pessoa, L.
- Affiliations
- University of Maryland, College Park (US)
- Corresponding author
- Pessoa, Luiz
- ORCID
- Pessoa, Luiz
- Venue
- Physics of Life Reviews
- Publisher
- Elsevier BV
- Published
- 2014-09
- Volume
- 11
- Issue
- 3
- Pages
- 400-435
- ISSN
- 1571-0645 (ISSN-L)
- PMC
- PMC4157099
- OpenAlex
- W2031461126
- Cited by
- 464
- References
- 156
- Citations by year
- 2026: 31 · 2025: 48 · 2024: 47 · 2023: 59 · 2022: 45 · 2021: 37 · 2020: 31 · 2019: 30 · 2018: 26 · 2017: 35 · 2016: 39 · 2015: 19 · 2014: 16
- FWCI
- 3.52
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Physics of Life Reviews (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Institute of Mental Health (R01MH071589)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- brain networks, brain organization, structure-function mapping, brain-behavior relationship, network perspective, many-to-many mapping, community detection, subnetworks, hierarchical brain organization, brain regions
- MeSH terms
- Algorithms, Animals, Brain, Brain Mapping, Humans, Nerve Net
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
